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Introduction to Behavioural Data Science


Admission requirements

Not applicable.


The course is build around three themes. First, we will discuss the methodology of scientific research involving human behavior, covering the following aspects: deriving a verifiable research idea, selecting data collection methods, and determining reliability and validity. In part two, we will introduce different statistical philosophies for analyzing behavioral data. The following topics will be addressed: descriptive statistics, frequentist hypothesis testing, Bayesian hypothesis testing, cross-validation, and design analyses. To this end, we will use the statistical programming language R. Finally, in part three, meta-scientific themes inclunding pre-registration, reproducibility, and replicability will be discussed.

Course objectives

  • Understanding key concepts regarding methods and techniques of behavioral data science.

  • Applying different statistical philosophies (i.e., frequentist hypothesis testing, Bayesian hypothesis testing, and cross-validation).

  • Analyzing data in R.

  • Understanding key meta-scientific concepts.


Timetable Artificial Intelligence

You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudymap will automatically be displayed in MyTimetable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.

MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).

For more information, watch the video or go the the 'help-page' in MyTimetable. Pleas note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.

Mode of instruction

A two-hour lecture and a two-hour work group session per week.

Assessment method

The assessment involves

  • A written, clossed-book exam consisting of 40 multiple choice questions with four alternatives each, covering both theoretical knowledge as well as statistical calculations discussed in the lectures, and work group sessions.

  • An R skills assignment covering the various aspects of students’ skills in working with R as well as in describing and interpreting statistical output.

The final grade is a weighted average of the examination grade (70%) and the grade for the R skills assignment (30%). However, in order to pass the course, students must get a grade of 5.5 or higher on both parts (henceforth partial grades). Students have the opportunity to retake the exam and/or the R skills assignment, if their respective partial grade(s) were below 5.5. If, after the resit, one or both partial grades are below 5.5, the student needs to retake the entire course.
The teacher will inform the students how the inspection of the exams will take place.

Reading list

Course material includes slides, exercises, and articles that will be made available via the online course platform.


From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudymap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.
Extensive FAQ on MyStudymap can be found here.


Dr. T.D.P. Heyman
Dr. S.M.H. Huisman

Education coordinator, Education coordinator LIACS bachelors


Not applicable.